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As the first step in the HIV care continuum, timely diagnosis is central to reducing transmission of the virus and ending the HIV epidemic. Studies have shown that distance from a testing site is essential for ease of access to services and educational material. This study shows how location-allocation analysis can be used to improve allocation of HIV testing services utilizing existing publicly available data from 2015 to 2019 on HIV prevalence, testing site location, and factors related to HIV in Philadelphia, Pennsylvania.The ArcGIS Location-Allocation analytic tool was used to calculate locations for HIV testing sites using a method that minimizes the distance between demand-point locations and service facilities. ZIP code level demand was initially specified on the basis of the percentage of late HIV diagnoses and in a sensitivity analysis on the basis of a composite of multiple factors. Travel time and distance from demand to facilities determined the facility location allocation. This analysis was conducted from 2021 to 2022.Compared with the 37 facilities located in 20 (43%) Philadelphia ZIP codes, the model proposed reallocating testing facilities to 37 (79%) ZIP codes using percent late diagnoses to define demand. On average, this would reduce distance to the facilities by 65% and travel time to the facilities by 56%. Results using the sensitivity analysis were similar.A wider distribution of HIV testing services across the city of Philadelphia may reduce distance and travel time to facilities, improve accessibility of testing, and in turn increase the percentage of people with knowledge of their status.

In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (Am J Public Health. 2022;112(10):1436–1445. https://doi.org/10.2105/AJPH.2022.306917 )

Introduction In July 2018, the U.S. Department of Housing and Urban Development passed a rule requiring public housing authorities to implement smoke-free housing (SFH) policies. We measured secondhand smoke (SHS) exposure immediately before, and repeatedly up to 36 months post-SFH policy implementation in a purposeful sample of 21 New York City (NYC) high-rise buildings (>15 floors): 10 NYC Housing Authority (NYCHA) buildings subject to the policy and 11 privately managed buildings in which most residents received housing vouchers (herein “Section 8”). Aims and Methods We invited participants from nonsmoking households (NYCHA n = 157, Section-8 n = 118) to enroll in a longitudinal air monitoring study, measuring (1) nicotine concentration with passive, bisulfate-coated filters, and (2) particulate matter (PM2.5) with low-cost particle sensors. We also measured nicotine concentrations and counted cigarette butts in common areas (n = 91 stairwells and hallways). We repeated air monitoring sessions in households and common areas every 6 months, totaling six post-policy sessions. Results After 3 years, we observed larger declines in nicotine concentration in NYCHA hallways than in Section-8, [difference-in-difference (DID) = −1.92 µg/m3 (95% CI –2.98, −0.87), p = .001]. In stairwells, nicotine concentration declines were larger in NYCHA buildings, but the differences were not statistically significant [DID= −1.10 µg/m3 (95% CI −2.40, 0.18), p = .089]. In households, there was no differential change in nicotine concentration (p = .093) or in PM2.5 levels (p = .385). Conclusions Nicotine concentration reductions in NYCHA common areas over 3 years may be attributable to the SFH policy, reflecting its gradual implementation over this time. Implications Continued air monitoring over multiple years has demonstrated that SHS exposure may be declining more rapidly in NYCHA common areas as a result of SFH policy adherence. This may have positive implications for improved health outcomes among those living in public housing, but additional tracking of air quality and studies of health outcomes are needed. Ongoing efforts by NYCHA to integrate the SFH policy into wider healthier-homes initiatives may increase policy compliance.

Infants born with low or high ("at-risk") birthweights are at greater risk of adverse health outcomes across the life course. Our objective was to examine whether geographic hotspots of low and high birthweight prevalence in New York City had different patterns of neighborhood risk factors. We performed census tract-level geospatial clustering analyses using (1) birthweight prevalence and maternal residential address from an all-payer claims database and (2) domains of neighborhood risk factors (socioeconomic and food environment) from national and local datasets. We then used logistic regression analysis to identify specific neighborhood risk factors associated with low and high birthweight hotspots. This study examined 2088 census tracts representing 419,025 infants. We found almost no overlap (1.5%) between low and high birthweight hotspots. The majority of low birthweight hotspots (87.2%) overlapped with a socioeconomic risk factor and 95.7% overlapped with a food environment risk factor. Half of high birthweight hotspots (50.0%) overlapped with a socioeconomic risk factor and 48.8% overlapped with a food environment risk factor. Low birthweight hotspots were associated with high prevalence of excessive housing cost, unemployment, and poor food environment. High birthweight hotspots were associated with high prevalence of uninsured persons and convenience stores. Programs and policies that aim to prevent disparities in infant birthweight should examine the broader context by which hotspots of at-risk birthweight overlap with neighborhood risk factors. Multi-level strategies that include the neighborhood context are needed to address prenatal pathways leading to low and high birthweight outcomes.

Study Objectives Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC). Methods We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities. Results Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. Conclusions Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.

Background: Blood pressure (BP) control is suboptimal in minority communities, including Asian populations. We evaluate the feasibility, adoption, and effectiveness of an integrated CHW-led health coaching and practice-level intervention to improve hypertension control among South Asian patients in New York City, Project IMPACT (Integrating Million Hearts for Provider and Community Transformation). The primary outcome was BP control, and secondary outcomes were systolic BP and diastolic BP at 6-month follow-up. Methods: A randomized-controlled trial took place within community-based primary care practices that primarily serve South Asian patients in New York City between 2017 and 2019. A total of 303 South Asian patients aged 18–85 with diagnosed hypertension and uncontrolled BP (systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg) within the previous 6 months at 14 clinic sites consented to participate. After completing 1 education session, individuals were randomized into treatment (n=159) or control (n=144) groups. Treatment participants received 4 additional group education sessions and individualized health coaching over a 6-month period. A mixed effect generalized linear model with a logit link function was used to assess intervention effectiveness for controlled hypertension (Yes/No), adjusting for practice level random effect, age, sex, baseline systolic BP, and days between BP measurements. Results: Among the total enrolled population, mean age was 56.8±11.2 years, and 54.1% were women. At 6 months among individuals with follow-up BP data (treatment, n=154; control, n=137), 68.2% of the treatment group and 41.6% of the control group had controlled BP ( P Conclusions: A CHW-led health coaching intervention was effective in achieving BP control among South Asian Americans in New York City primary care practices. Findings can guide translation and dissemination of this model across other communities experiencing hypertension disparities. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03159533

OBJECTIVE We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract–level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural. RESULTS Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE–diabetes associations were mediated through food-environment pathways. CONCLUSIONS Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways.

IntroductionThe literature is limited on the impact of neighborhood parks on quality of life (QoL) and the mechanism linking them.MethodsIn this paper, we applied the structural equation model to data from a cross-sectional sample of 650 participants in low-income communities of New York City, we examined the associations of neighborhood park use vs. park perception and QoL, and whether these associations were mediated through self-reported perceived stress. We also examined whether park use mediated the relationship between park perception and QoL.ResultsWe found that park use had a significant but weak association with QoL (standardized β = 0.08, 95% confidence interval (CI): 0.02, 0.15, p = 0.02), but this relationship was not mediated by self-reported stress. Park perception was more strongly associated with QoL than park use (standardized β = 0.23, 95% CI: 0.16, 0.30, p < 0.01), and this was partly mediated by self-reported stress (indirect effect- standardized β = 0.08, 95% CI: 0.03, 0.13, p < 0.01) and, to a lesser extent, by park use (indirect effect- standardized β = 0.01, 95% CI: 0.00, 0.02, p = 0.01).DiscussionHaving well-perceived parks appears to be an important factor for QoL independent of park use, suggesting that quality parks may benefit everyone in a community beyond park users. This strengthens the argument in favor of increasing park investment as a strategy to improve population wellbeing.

IntroductionThe onslaught of the COVID-19 pandemic has placed severe demands on US health systems and the health care workforce. In New York State (NYS) and New York City (NYC), the early American epicenter, hospitals ran the risk of exhausting supplies of ventilators, ICU beds, and personal protective equipment (PPE); the capacity of the healthcare workforce to meet the added demand was equally strained. The COVID-19 Healthcare Personnel Study (CHPS) was designed to assess adverse short and long-term physical and mental health impacts of the COVID-19 pandemic on New York’s physicians, nurse practitioners, and physician assistants.MethodsPopulation-based online survey of physicians, nurse practitioners and physician assistants registered with the New York State Department of Health. Survey-weighted descriptive results were analyzed using frequencies, proportions, and means, with 95% confidence intervals. Odds ratios were calculated for association using survey-weighted logistic regression.ResultsOver half (51.5%; 95% CI 49.1, 54.0) of respondents worked directly with COVID-19 patients. Among those tested, 27.3% (95% CI 22.5, 32.2) were positive. The majority (57.6%; 95% CI 55.2, 60.0) of respondents reported that the COVID-19 pandemic had a negative impact on their mental health. Negative mental health was associated with experiencing symptoms of COVID-19 (OR=1.7, 95% CI 1.3, 2.1) and redeployment to unfamiliar functions. (OR=1.3, 95% CI 1.1, 1.6).ConclusionsA majority of New York healthcare providers were involved in treating COVID-19 patients and reported that the pandemic had a negative impact on their mental health.

In response to fast-turnaround funding opportunities, collaborations have been forming across the country to address severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disparities. Here we describe the process, notes from the field, and evaluation results from a new collaboration involving multiple partners, formed in October 2020 in New York City as part of the Rapid Acceleration of Diagnostics initiative. We used the validated Research Engagement Survey Tool to evaluate the partnership. Results can inform future research and improve engagement efforts aimed at reducing SARS-CoV-2 disparities. (Am J Public Health. 2022;112(S9):S904–S908. https://doi.org/10.2105/AJPH.2022.307072 )

How online grocery shopping behaviors differ among Asian American (AA) ethnic subgroups and acculturation level is unknown. From June 9-15, 2020, we administered an online survey to a nationally-derived nonprobability sample of 2,895 AA adults, including 1,737 East, 570 South, and 587 Southeast Asian adults, assessing online grocery shopping (yes/no, frequency, reasons). We used logistic regression to compare responses by subgroup and acculturation score, controlling for sociodemographics. Thirty-percent of participants reported shopping online for groceries in a typical month, with a higher percentage among South (45%) versus East Asian adults (23%). Participants with low (vs. high) acculturation scores were more likely to report a lack of special foods (OR = 0.7; 95% CI: 0.5-0.98) and poor food quality (OR = 0.6; 95% CI: 0.4-0.7) as preventing them from shopping online. Online grocery shopping has the capacity to address inequities in health, potentially via culturally-tailored programs designed for less-acculturated AA adults.

While health care-associated financial burdens among uninsured individuals are well described, few studies have systematically characterized the array of financial and logistical complications faced by insured individuals with low household incomes. In this mixed methods paper, we conducted 6 focus groups with a total of 55 residents and analyzed programmatic administrative records to characterize the specific financial and logistic barriers faced by residents living in public housing in East and Central Harlem, New York City (NYC). Participants included individuals who enrolled in a municipal community health worker (CHW) program designed to close equity gaps in health and social outcomes. Dedicated health advocates (HAs) were explicitly paired with CHWs to provide health insurance and health care navigational assistance. We describe the needs of 150 residents with reported financial barriers to care, as well as the navigational and advocacy strategies taken by HAs to address them. Finally, we outline state-level policy recommendations to help ameliorate the problems experienced by participants. The model of paired CHW–HAs may be helpful in addressing financial barriers for insured populations with low household income and reducing health disparities in other communities.

Although microbial populations in the gut microbiome are associated with COVID-19 severity, a causal impact on patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. We first demonstrate SARS-CoV-2 infection induces gut microbiome dysbiosis in mice, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, including blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19.

Background and ObjectivesRecent studies have suggested that intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history.MethodsParticipants were from 11 sites within the International Multiple Sclerosis Visual System consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with a history of ON among PwMS. Receiver operating characteristic (ROC) curve analysis was performed on a training data set (2/3 of cohort) and then applied to a testing data set (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT.ResultsAmong 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs controls. This composite score performed best, with area under the curve (AUC) = 0.89 (95% CI 0.85–0.93), sensitivity = 81%, and specificity = 80%. The composite score ROC curve performed better than any of the individual measures from the model (p < 0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC = 0.77, 95% CI 0.71–0.83, sensitivity = 68%, specificity = 77%). SVM analysis performed comparably with standard logistic regression models.DiscussionA composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with a history of unilateral ON. SVM performed as well as standard statistical models for these classifications.Classification of EvidenceThis study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared with clinical criteria.

No brief dietary screeners are available that capture dietary consumption patterns of Asian Americans. The purpose of this article is to describe the cultural adaptation of the validated Dietary Screener Questionnaire (DSQ) for use by clinicians, researchers, and community-based partners seeking to understand and intervene on dietary behaviors among English-speaking Asian Americans, for the six largest Asian subgroups (Chinese, Filipino, Asian Indian, Vietnamese, Korean, Japanese) in the United States. This was mainly accomplished by adding culturally specific examples of foods to the questionnaire items via searching online databases and soliciting input from members of our community partner network representing each of the six largest Asian subgroups. Over half of the 26 items on the DSQ were modified to include more culturally specific foods. Developing high-quality tools that reflect the diversity of the U.S. population are critical to implement nutrition interventions that do not inadvertently widen health disparities.

Introduction: The US Asian American (AA) population is projected to double by 2050, reaching ~43 million, and currently resides primarily in urban areas. Despite this, the geographic distribution of AA subgroup populations in US cities is not well-characterized, and social determinants of health (SDH) and health measures in places with significant AA/AA subgroup populations have not been described. Our research aimed to: 1) map the geographic distribution of AAs and AA subgroups at the city- and neighborhood- (census tract) level in 500 large US cities (population ≥66,000); 2) characterize SDH and health outcomes in places with significant AA or AA subgroup populations; and 3) compare SDH and health outcomes in places with significant AA or AA subgroup populations to SDH and health outcomes in places with significant non-Hispanic White (NHW) populations. Methods: Maps were generated using 2019 Census 5-year estimates. SDH and health outcome data were obtained from the City Health Dashboard, a free online data platform providing more than 35 measures of health and health drivers at the city and neighborhood level. T-tests compared SDH (unemployment, high-school completion, childhood poverty, income inequality, racial/ ethnic segregation, racial/ethnic diversity, percent uninsured) and health outcomes (obesity, frequent mental distress, cardiovas­cular disease mortality, life expectancy) in cities/neighborhoods with significant AA/AA subgroup populations to SDH and health outcomes in cities/neighborhoods with sig­nificant NHW populations (significant was defined as top population proportion quin­tile). We analyzed AA subgroups including Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other AA. Results: The count and proportion of AA/ AA subgroup populations varied sub­stantially across and within cities. When comparing cities with significant AA/AA subgroup populations vs NHW populations, there were few meaningful differences in SDH and health outcomes. However, when comparing neighborhoods within cities, areas with significant AA/AA subgroup vs NHW populations had less favorable SDH and health outcomes. Conclusion: When comparing places with significant AA vs NHW populations, city-level data obscured substantial variation in neighborhood-level SDH and health outcome measures. Our findings empha­size the dual importance of granular spatial and AA subgroup data in assessing the influence of SDH in AA populations. Ethn Dis. 2021;31(3):433-444; doi:10.18865/ed.31.3.433

Purpose In this study we aim to estimate the change in metabolic syndrome (MetS) prevalence among New York City (NYC) adults between 2004 and 2013–2014 and identify key subgroups at risk. Methods We analyzed data from NYC Health and Nutrition Examination Survey. MetS was defined as having at least three of the following: abdominal obesity, low HDL, elevated triglycerides, glucose dysregulation, and elevated blood pressure. We calculated age-standardized MetS prevalence, change in prevalence over time, and prevalence ratios by gender and race/ethnicity groups. We also tested for additive interaction. Results In 2013–2014 MetS prevalence among NYC adults was 24.4% (95% CI, 21.4–27.6). Adults 65+ years and Asian adults had the highest prevalence (45.6% and 33.8%, respectively). Abdominal obesity was the most prevalent MetS component in 2004 and 2013–2014 (50.7% each time). Between 2004 and 2013–2014, MetS decreased by 18.2% (P = .04) among women. The decrease paralleled similar declines in elevated triglycerides and glucose dysregulation. In 2013–14, non-Latino Black women had higher risk of MetS than non-Latino Black men and non-Latino White adults. Conclusion Age and racial/ethnic disparities in MetS prevalence in NYC were persistent from 2004 to 2013–2014, with Asian adults and non-Latino Black women at particularly high risk.

Objectives Immigrant adults tend to have better health than native-born adults despite lower incomes, but the health advantage decreases with length of residence. To determine whether immigrant adults have a health advantage over US-born adults in New York City, we compared cardiovascular disease (CVD) risk factors among both groups. Methods Using data from the New York City Health and Nutrition Examination Survey 2013-2014, we assessed health insurance coverage, health behaviors, and health conditions, comparing adults ages ≥20 born in the 50 states or the District of Columbia (US-born) with adults born in a US territory or outside the United States (immigrants, following the National Health and Nutrition Examination Survey) and comparing US-born adults with (1) adults who immigrated recently (≤10 years) and (2) adults who immigrated earlier (>10 years). Results For immigrant adults, the mean time since arrival in the United States was 21.8 years. Immigrant adults were significantly more likely than US-born adults to lack health insurance (22% vs 12%), report fair or poor health (26% vs 17%), have hypertension (30% vs 23%), and have diabetes (20% vs 11%) but significantly less likely to smoke (18% vs 27%) (all P < .05). Comparable proportions of immigrant adults and US-born adults were overweight or obese (67% vs 63%) and reported CVD (both 7%). Immigrant adults who arrived recently were less likely than immigrant adults who arrived earlier to have diabetes or high cholesterol but did not differ overall from US-born adults. Conclusions Our findings may help guide prevention programs and policy efforts to ensure that immigrant adults remain healthy.

Coronavirus disease 2019 symptom definitions rarely include symptom severity. We collected daily nasal swab samples and symptom diaries from contacts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case patients. Requiring ≥1 moderate or severe symptom reduced sensitivity to predict SARS-CoV-2 shedding from 60.0% (95% confidence interval [CI], 52.9%−66.7%) to 31.5% (95% CI, 25.7%− 38.0%) but increased specificity from 77.5% (95% CI, 75.3%−79.5%) to 93.8% (95% CI, 92.7%−94.8%).

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Objective Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks. Materials and methods This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009–2017) and from the Health and Retirement Survey (HRS, 1995–2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores. Results The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR. Discussion and conclusion EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.

Objective: s rates of childhood obesity and pediatric type 2 diabetes (T2D) increase, a better understanding is needed of how these two conditions relate, and which subgroups of children are more likely to develop diabetes with and without obesity. Methods: To compare hotspots of childhood obesity and pediatric T2D in New York City, we performed geospatial clustering analyses on obesity estimates obtained from surveys of school-aged children and diabetes estimates obtained from healthcare claims data, from 2009-2013. Analyses were performed at the Census tract level. We then used multivariable regression analysis to identify sociodemographic and environmental factors associated with these hotspots. Results: We identified obesity hotspots in Census tracts with a higher proportion of Black or Hispanic residents, with low median household income, or located in a food swamp. 51.1% of pediatric T2D hotspots overlapped with obesity hotspots. For pediatric T2D, hotspots were identified in Census tracts with a higher proportion of Black residents and a lower proportion of Hispanic residents. Conclusions: Non-Hispanic Black neighborhoods had a higher probability of being hotspots of both childhood obesity and pediatric type 2 diabetes. However, we identified a discordance between hotspots of childhood obesity and pediatric diabetes in Hispanic neighborhoods, suggesting either under-detection or under-diagnosis of diabetes, or that obesity may influence diabetes risk differently in these two populations. These findings warrant further investigation of the relationship between childhood obesity and pediatric diabetes among different racial and ethnic groups, and may help guide pediatric public health interventions to specific neighborhoods.

Objectives The purpose of this study was to examine socio-demographic and psychosocial correlates of non-adherence to an accelerometry protocol in an economically disadvantaged urban population. Design Cross-sectional study. Methods We analyzed 985 New York City adult participants aged 18–81 years from the Physical Activity and Redesigned Community Spaces (PARCS) study. Participants were asked to wear a hip-worn ActiGraph GT3X-BT accelerometer for one week. Adherent accelerometer wear was defined as ≥3 days of ≥8 h/day of wear over a 7-day period and non-adherent accelerometry wear was defined as any wear less than adherent wear from returned accelerometers. Examined correlates of adherence included sociodemographic and psychosocial characteristics (e.g., general physical/mental health-related quality of life, self-efficacy for exercise, stress, sense of community/neighborhood well-being, and social cohesion). Results From the total sample, 636 (64.6%) participants provided adherent wear and 349 (35.4%) provided non-adherent wear. In multivariable analysis, younger age (odds ratio [OR] = 0.63, 95% confidence interval [CI]: 0.53–0.75), poorer health-related quality of life (OR = 0.80, 95% CI: 0.65–0.98 for physical health and OR = 0.77, 95% CI: 0.62–0.94 for mental health), lower sense of community (OR = 0.79, 95% CI: 0.62–1.00) and current smoking status (OR = 1.97, 95% CI: 1.35–2.86) were associated with non-adherent wear. Conclusions Non-adherent wear was associated with younger age, smoking, and lower self-reported physical/mental functioning and sense of community. This information can inform targeted adherence strategies to improve physical activity and sedentary behavior estimates from accelerometry data in future studies involving an urban minority population.

Introduction Perceived stress is linked to poor sexual and reproductive health, but its relationship with sexually transmitted infections (STIs) is less clear. The elevated burden of stress and STI among Black women suggests a need to examine racial differences in the associations on additive and multiplicative scales. Methods Using data from Black and White female participants from wave IV of the National Longitudinal Study of Adolescent to Adult Health (n = 4744), we examined the association of high stress (scores ≥6 on the Perceived Stress Scale-4) with self-reported past-year chlamydia diagnosis, combined curable STI, and lifetime pelvic inflammatory disease using modified Poisson regression with robust variance to estimate prevalence ratios and prevalence differences. Models included a race-stress product-interaction term and adjusted for sociodemographic variables, prior trauma and stressors, and mental health factors. Results In unadjusted analyses, stress was associated with STI among Black and White women. Adjusted associations were attenuated among White women; among Black women, stress remained associated with chlamydia (adjusted prevalence ratio, 2.22; 95% confidence interval, 1.30-3.79) and curable STI (adjusted prevalence ratio, 1.59; 95% confidence interval, 1.05-2.40), corresponding to approximately 5 excess cases of each. Among White women, poverty and personality traits were the strongest confounders; among Black women, poverty, trauma, and neurotic personality traits were the strongest confounders for chlamydia, although no factors seemed to confound the association with curable STI. Conclusions Stress is independently linked to STI, particularly among Black women. Additional research with longitudinal data is needed to understand the role of stress on STI and address a significant health disparity.

Air pollution is a major contributor to cardiovascular morbidity and mortality. Fine particulate matter P =0.01) but a nonsignificant difference in mean diastolic BP (−0.95 mm Hg [95% CI, −2.81 to 0.91]; P =0.32). Subgroup analyses indicated no heterogeneity of effect by age, level of particulate exposure, or study duration. Given the variation in study design, additional study is warranted to confirm and better quantify the observed benefits in systolic BP found with personal air cleaner use.

Diabetes and hypertension are socially patterned by individual race/ethnicity and by neighborhood economic context, but distributions among Asian subgroups are under-characterized. We examined variation in prevalence for both conditions, comparing between US Asian subgroups, including within South Asian nationalities, and comparing within subgroups by neighborhood economic context. We obtained data on a nonprobability sample of 633,664 patients ages 18–64 in New York City, New York, USA (2014–2017); 30,138 belonged to one of seven Asian subgroups (Asian Indian, Bangladeshi, Pakistani, Chinese, Korean, Japanese, and Filipino). We used electronic health records to classify disease status. We characterized census tract economic context using the Index of Concentration at the Extremes and estimated prevalence differences using multilevel models. Among Asian men, hypertension prevalence was highest for Filipinos. Among Asian women, hypertension prevalence was highest for Filipinas and Bangladeshis. Diabetes prevalence was highest among Pakistanis and Bangladeshis of both genders, exceeding all other Asian and non-Asian groups. There was consistent evidence of an economic gradient for both conditions, whereby persons residing in the most privileged neighborhood tertile had the lowest disease prevalence. The economic gradient was particularly strong for diabetes among Pakistanis, whose prevalence in the most deprived tertile exceeded that of the most privileged by 9 percentage points (95% CI: 3, 14). Only Koreans departed from the trend, experiencing the highest diabetes prevalence in the most privileged tertile. US Asian subgroups largely demonstrate similar neighborhood economic gradients as other groups. Disaggregating Asian subgroups, including within South Asian nationalities, reveals important heterogeneity in prevalence.

Neighborhood walkability (NW) has been linked to increased physical activity, which in turn is associated with lower concentrations of sex hormones and higher concentration of SHBG in women. However, no study has directly examined the association of NW with female sex hormone levels.We conducted a cross-sectional study to evaluate the association between NW and circulating levels of sex hormones and SHBG in pre- and post-menopausal women.We included 797 premenopausal and 618 postmenopausal women from the New York University Women's Health Study (NYUWHS) who were healthy controls in previous nested case-control studies in which sex hormones (androstenedione, testosterone, DHEAS, estradiol and estrone) and SHBG had been measured in serum at enrollment. Baseline residential addresses were geo-coded and the Built Environment and Health Neighborhood Walkability Index (BEH-NWI) was calculated. Generalized Estimating Equations were used to assess the association between BEH-NWI and sex hormone and SHBG concentrations adjusting for individual- and neighborhood-level factors.In premenopausal women, a one standard deviation (SD) increment in BEH-NWI was associated with a 3.5% (95% CI 0.9%-6.1%) lower DHEAS concentration. In postmenopausal women, a one SD increment in BEH-NWI was related to an 8.5% (95% CI 5.4%-11.5%) lower level of DHEAS, a 3.7% (95% CI 0.5%-6.8%) lower level of testosterone, a 1.8% (95% CI 0.5%-3.0%) lower level of estrone, and a 4.2% (95% CI 2.7%-5.7%) higher level of SHBG. However, the associations with respect to DHEAS and estrone became apparent only after adjusting for neighborhood-level variables. Sensitivity analyses using fixed effects meta-analysis and inverse probability weighting accounting for potential selection bias yielded similar results.Our findings suggest that NW is associated with lower concentrations of androgens and estrone, and increased SHBG, in postmenopausal women, and lower levels of DHEAS in premenopausal women.

ImportanceDespite recent growth in online redemption of Supplemental Nutrition Assistance Program (SNAP) benefits, no previous work has tested the impact of economic and behavioral economic strategies on food purchasing behaviors in an online grocery retail setting among adults with low income.ObjectiveTo examine the extent to which financial incentives and default shopping cart options influence fruit and vegetable purchases.Design, Setting, and ParticipantsThis randomized clinical trial used an experimental online grocery store for adults who currently or have ever received SNAP benefits. From October 7 to December 2, 2021, participants were instructed to shop for a week’s worth of groceries for their household, with a budget tailored to household size; no payment was taken.InterventionsRandom assignment to 1 of 4 conditions: no intervention, 50% discount on eligible fruits and vegetables, prefilled shopping carts with tailored fruit and vegetable items (ie, default options), or a combination of the discount and default options.Main Outcomes and MeasuresThe primary outcome was the percentage of nondiscounted dollars spent on eligible fruit and vegetables per basket.ResultsOf 2744 participants, mean (SD) age was 46.7 (16.0) years, and 1447 (52.7%) identified as women. A total of 1842 participants (67.1%) reported currently receiving SNAP benefits and 1492 (54.4%) reported shopping online for groceries in the previous 12 months. Participants spent a mean (SD) 20.5% (23.5%) of total dollars on eligible fruits and vegetables. Compared with no intervention, those in the discount condition spent 4.7% (98.3% CI, 1.7%-7.7%) of more total dollars on eligible fruits and vegetables; those in the default condition, 7.8% (98.3% CI, 4.8%-10.7%) more; and those in the combination condition, 13.0% (98.3% CI, 10.0%-16.0%) more (P P = .06), but the effect in the combination condition was significantly larger than both discount and default conditions (P < .001). Default shopping cart items were purchased by 679 participants (93.4%) in the default condition and 655 (95.5%) in the combination condition, whereas 297 (45.8%) in the control and 361 (52.9%) in the discount conditions purchased those items (P < .001). No variation was observed by age, sex, or race and ethnicity, and results were similar when those who reported never shopping online for groceries were excluded.Conclusions and RelevanceIn this randomized clinical trial, financial incentives for fruits and vegetables and default options, especially in combination, led to meaningful increases in online fruit and vegetable purchases among adults with low income.Trial RegistrationClinicalTrials.gov Identifier: NCT04766034